Journal of the American College of Cardiology
© 2007 by the American College of Cardiology Foundation
Published by Elsevier Inc.
Vol. 49, No. 5, 2007
ISSN 0735-1097/07/$32.00
doi:10.1016/j.jacc.2006.08.060
Atrial Fibrillation
Obstructive Sleep Apnea, Obesity,
and the Risk of Incident Atrial Fibrillation
Apoor S. Gami, MD,*† Dave O. Hodge, MS,‡ Regina M. Herges, BS,‡ Eric J. Olson, MD,†§
Jiri Nykodym, BS,*† Tomas Kara, MD,*† Virend K. Somers, MD, PHD, FACC*†储
Rochester, Minnesota
Objectives
This study sought to identify whether obesity and obstructive sleep apnea (OSA) independently predict incident
atrial fibrillation/flutter (AF).
Background
Obesity is a risk factor for AF, and OSA is highly prevalent in obesity. Obstructive sleep apnea is associated with
AF, but it is unknown whether OSA predicts new-onset AF independently of obesity.
Methods
We conducted a retrospective cohort study of 3,542 Olmsted County adults without past or current AF who were
referred for an initial diagnostic polysomnogram from 1987 to 2003. New-onset AF was assessed and confirmed by electrocardiography during a mean follow-up of 4.7 years.
Results
Incident AF occurred in 133 subjects (cumulative probability 14%, 95% confidence interval [CI] 9% to 19%). Univariate predictors of AF were age, male gender, hypertension, coronary artery disease, heart failure, smoking,
body mass index, OSA (hazard ratio 2.18, 95% CI 1.34 to 3.54) and multiple measures of OSA severity. In subjects ⬍65 years old, independent predictors of incident AF were age, male gender, coronary artery disease, body
mass index (per 1 kg/m2, hazard ratio 1.07, 95% CI 1.05 to 1.10), and the decrease in nocturnal oxygen saturation (per 0.5 U log change, hazard ratio 3.29, 95% CI 1.35 to 8.04). Heart failure, but neither obesity nor OSA,
predicted incident AF in subjects ⱖ65 years of age.
Conclusions
Obesity and the magnitude of nocturnal oxygen desaturation, which is an important pathophysiological consequence of OSA, are independent risk factors for incident AF in individuals ⬍65 years of age. (J Am Coll Cardiol
2007;49:565–71) © 2007 by the American College of Cardiology Foundation
Atrial fibrillation/flutter (AF) is the most prevalent sustained cardiac arrhythmia in the U.S. (1) and is associated
with significant morbidity and mortality (2,3). In the U.S.,
the major clinical risk factors for AF include age, diabetes,
hypertension, heart failure, and coronary artery disease (4).
Long-term follow-up data of over 5,000 subjects in the
Framingham Heart Study (5) and nearly 48,000 subjects in
the Danish Diet, Cancer and Health Study (6) showed that
obesity predicts AF independently of other clinical characteristics. In the context of the growing obesity epidemic,
these data have important implications for the population
burden of AF. They also raise the important question of
how obesity leads to AF.
From the *Division of Cardiovascular Diseases, †Department of Internal Medicine,
‡Department of Biostatistics, §Division of Pulmonary and Critical Care Medicine,
and 储Division of Hypertension, Mayo Clinic College of Medicine, Rochester,
Minnesota. Dr. Somers is a consultant for Respironics, co-investigator for a study
funded with a grant from ResMed Foundation, and has received an honorarium from
ResMed. The authors are funded by NIH grants HL61560, HL65176, HL73211,
and M01-RR00585, and the Mayo Clinic.
Manuscript received March 30, 2006; revised manuscript received August 22, 2006,
accepted August 28, 2006.
In a recent cross-sectional study, we showed a strong
association between AF and obstructive sleep apnea (OSA),
independently of age, gender, hypertension, heart failure
and, importantly, body mass index (7). No prior study,
including the Framingham study and the Danish study, has
assessed the potential contributions of OSA to the risk of
AF associated with obesity. For several reasons, OSA may
have an important role in clarifying the relationship between
obesity and AF (8). First, obesity and OSA are powerfully
and causally related (9). Second, pathophysiological characteristics traditionally attributed to obesity have been shown
to be linked to occult OSA (10 –14). Third, OSA is
associated with multiple pathophysiological mechanisms
that may be directly implicated in the pathogenesis of AF
either by triggering its initiation or by affecting the
myocardial substrate to promote or maintain the arrhythmia. These mechanisms include repetitive and prolonged
hypoxemia, exaggerated intrathoracic pressure oscillations with increased cardiac wall stress (15–17), sympathetic and parasympathetic imbalances (18), systemic
inflammation (14), and diastolic dysfunction (19 –22). It
is unknown, however, whether OSA increases the risk of
566
Gami et al.
OSA, Obesity, and AF
Abbreviations
and Acronyms
AF ⴝ atrial
fibrillation/flutter
CI ⴝ confidence interval
JACC Vol. 49, No. 5, 2007
February 6, 2007:565–71
incident AF and how comorbid
conditions, including obesity,
may affect this relationship.
Methods
Study population. The Institutional Review Board of the Mayo
Foundation approved the study,
and we only included individuals
who authorized their records to
be used for research. Data were collected from subjects’
medical records by dedicated and specifically trained personnel in a cardiovascular research studies unit. Quality
control of data collection was maintained by supervision and
feedback to the research personnel by a study physician. A
validation series found excellent agreement between data of
a subgroup of subjects collected blindly by both a study
physician and the trained research personnel (Cohen kappa
0.91) (23).
We identified adult residents of Olmsted County, Minnesota, who underwent their first diagnostic polysomnography at the Mayo Clinic Sleep Disorders Center from July
1, 1987, to July 31, 2003. During these years, the Mayo
Clinic was the sole provider, in Olmsted County, of a
comprehensive sleep disorders evaluation, with initial consultation by a sleep specialist, complete overnight polysomnography, study review with the sleep specialist, and longterm management of treatment when necessary. We
excluded people with AF or a previous history of AF at the
time of their first-ever polysomnogram because we wished
to assess the risk of incident AF after the initial diagnosis of
OSA. Data were collected regarding demographics, anthropomorphic measurements, and relevant comorbid conditions at the time of polysomnography for each subject.
Classification of OSA. All subjects’ sleep evaluations were
performed at the Mayo Clinic Sleep Disorders Center, an
American Academy of Sleep Medicine-accredited sleep
center with real-time monitoring of polysomnography by
certified sleep technicians and supervision by board-certified
sleep specialists. Each evaluation consisted of consultation
with a board-certified sleep physician and split-night polysomnography, in which the first half of the night is a
diagnostic study consisting of measurements of the electroencephalogram, electro-oculogram, electromyogram, electrocardiogram, thoracoabdominal excursions, pulse oximetry, and naso-oral airflow. Physiological variables collected
from the polysomnograms included sleep efficiency; arousal
index; the number of central apneas, obstructive or mixed
apneas, and hypopneas per hour of rapid eye movement and
non-rapid eye movement sleep; oxygen saturation during
wakefulness; mean oxygen saturation during sleep; and the
lowest oxygen saturation during sleep. Apneas and hypopneas were defined as obstructive events if occurring in the
presence of thoracoabdominal excursions representing ventilatory efforts, and the apnea-hypopnea index was calcuHR ⴝ hazard ratio
OSA ⴝ obstructive sleep
apnea
lated as the average number of obstructive apneas and
hypopneas per hour of sleep. We defined OSA as an
apnea-hypopnea index ⱖ5 (24).
We ascertained the use of continuous positive airway
pressure therapy during follow-up. We considered that
continuous positive airway pressure had been used if it was
prescribed during the consultation with the sleep specialist
after the sleep study and if a subsequent note in the medical
record stated that continuous positive airway pressure was
being used.
Classification of AF. The occurrence of incident AF was
ascertained from the date of polysomnography to the date of
death or last follow-up (which was the date of the last clinic
visit, nursing home visit, emergency department visit, surgical procedure, hospitalization, or laboratory evaluation) by
querying the Mayo Clinic electronic medical index. The
Mayo Clinic uses a unified medical record that includes the
details of all inpatient and outpatient encounters, laboratory
reports, and correspondence for every patient (25). Diagnoses made during office visits, clinic consultations, nursing
home care, emergency department visits, surgical procedures, hospitalizations, and autopsy examinations are listed
on a master sheet, assigned a modified hospital International Classification of Disease adaptation code, and included in a central, electronic, searchable diagnostic index
(25). We cross-referenced our study sample with all medical
index diagnostic codes for acute, paroxysmal, and chronic
atrial fibrillation; atrial fibrillation-flutter; and atrial flutter.
For quality assurance, we also manually reviewed the full
medical record of each subject with AF identified by the
above methods. We established the occurrence of AF only
when a study physician confirmed the diagnosis by review of
an electrocardiogram. We were interested in the occurrence
of incident AF, and thus did not assess the duration of AF
and made no distinction between paroxysmal, persistent, or
permanent AF. The collection of follow-up data and confirmation of AF were performed in a blinded fashion to the
collection of polysomnographic data and the classification of
OSA.
Statistical analysis. Characteristics of the study population
were described by counts (with percentages) and means
(with standard deviations) or medians (with interquartile
ranges).
Time-to-event analyses were performed using KaplanMeier methods to identify univariate predictors of incident
AF. The parameters included subject age, gender, body
mass index, relevant comorbidities, OSA status and severity,
and physiological sleep variables. Multivariate time-to-event
analyses were performed using Cox proportional hazards
regression methods. A stepwise selection technique was
used to construct a clinical model with parameters that are
well-established clinical predictors of AF, including age,
gender, smoking history, hypertension, diabetes, coronary
artery disease, and heart failure. We also included body mass
index as a potential predictor in this model.
Gami et al.
OSA, Obesity, and AF
JACC Vol. 49, No. 5, 2007
February 6, 2007:565–71
To assess the risk of AF posed by OSA and its related
sleep parameters, we included these variables into the model
independently of one another: OSA status, OSA severity,
the apnea-hypopnea index, arousal index, awake oxygen
saturation, lowest nocturnal oxygen saturation, mean nocturnal oxygen saturation, and decrease in nocturnal oxygen
saturation (defined as the awake oxygen saturation minus
mean nocturnal oxygen saturation). Continuous variables
with a non-Gaussian distribution were logarithmically
transformed for analysis.
We tested for effect modification of the dependent variable
by interactions between OSA and gender or age by incorporating multiplicative interaction terms (OSA ⫻ gender and
OSA ⫻ age) into the regression models. The rationale for this
was the established increase of risk in men for both OSA
(26) and AF (4), and the recognized nonmonotonic association between OSA and cardiovascular morbidity and mortality across the adult ages (27,28). Prior studies found that
significant relationships between OSA and cardiovascular
morbidity and mortality existed in adults through age 60 or
65 years, and then became nonsignificant in older patients
(27,28). Thus, we performed multivariate analyses using
Cox proportional hazards regression methods, as described
above, to create additional models incorporating clinical
predictors and physiological sleep parameters for subjects
⬍65 years of age and for subjects ⱖ65 years of age.
We performed an analysis that included only subjects
with OSA to identify the effect of continuous positive
airway pressure use on the risk of incident AF. The use of
continuous positive airway pressure was introduced into a
multivariate model, which was created as described above to
include the clinical predictors and sleep measurements. We
performed an additional analysis to assess the simultaneous
effects of both obesity and OSA, regardless of other comorbidities, on incident AF by comparing the cumulative
probability of incident AF across 9 groups of subjects
characterized by their apnea-hypopnea index (⬍5, 5 to 40,
and ⱖ40) and body mass index (⬍25, 25 to 30, and ⱖ30
kg/m2). Also, to evaluate whether greater follow-up in
patients with OSA could account for increased identification of AF in these patients, we performed linear regression
analyses to identify an association between follow-up duration and the apnea-hypopnea index, and to compare
follow-up duration in patients without OSA and patients
with increasing severities of OSA.
Analyses were performed using SAS Proprietary Software
Release 8.2 (1999 to 2000, SAS Institute, Cary, North
Carolina). A 2-sided p value ⬍0.05 was considered statistically significant for all analyses. The authors had full access
to the data and take responsibility for its integrity; all
authors have read and agree to the article as written.
Results
Study population. The study population consisted of
3,542 subjects, and their baseline characteristics are de-
567
Baseline Characteristics of Patients
Table 1
Baseline Characteristics of Patients
Characteristic
Age, mean (SD), yrs
Male gender, n (%)
Body mass index, mean (SD), kg/m2
Coronary artery disease, n (%)
Heart failure, n (%)
Hypertension, n (%)
Diabetes mellitus, n (%)
Value
49 (14)
2,349 (66)
33 (9)
342 (10)
89 (3)
1,178 (33)
397 (11)
History of smoking, n (%)
1,798 (56)
Obstructive sleep apnea, n (%)
2,626 (74)
Lowest nocturnal oxygen saturation, %
Mean (SD)
Median
83 (10)
87
Mean nocturnal oxygen saturation, %
Mean (SD)
Median
93 (5)
94
Decrease in nocturnal oxygen saturation,* %
Mean (SD)
Median
12 (10)
9
Apnea–hypopnea index, events/h
Mean (SD)
Median
26.7 (31.3)
15.0
Arousal index, events/h
Mean (SD)
Median
35.4 (26.1)
28.0
*This variable is defined as the difference between awake oxygen saturation and mean nocturnal
oxygen saturation.
scribed in Table 1. Obstructive sleep apnea (defined by an
apnea-hypopnea index ⱖ5) was present in 2,626 subjects
(74%), and their mean apnea-hypopnea index was 36 (⫾32),
mean awake oxygen saturation was 96% (⫾3%), mean
nocturnal oxygen saturation was 93% (⫾4%), and lowest
nocturnal oxygen saturation was 81% (⫾11%). Use of
continuous positive airway pressure was reported for 123
of 695 (18%), 190 of 636 (30%), 253 of 648 (39%), and 338
of 647 (52%) subjects with increasing severity of OSA based
on quartiles of the apnea-hypopnea index. Bivariate linear
regression showed no association between follow-up time
and the apnea-hypopnea index (p ⫽ 0.87). Follow-up
duration was similar for patients without OSA (4.76 years)
and for patients with increasing severities of OSA based on
quartiles of the apnea-hypopnea index (4.66 vs. 4.79 vs. 4.50
vs. 4.59 years; analysis of variance p ⫽ 0.58).
Incidence of AF. After an average follow-up of 4.7 years
(up to 15 years), incident AF occurred in 133 subjects
(cumulative frequency 14%, 95% confidence interval [CI]
9% to 19%). Clinical characteristics and sleep measurements that predicted incident AF are listed in Table 2.
These included established risk factors for AF, such as
male gender, age, hypertension, coronary artery disease,
heart failure, and smoking. In addition, body mass index
strongly predicted incident AF (per 1 kg/m2, hazard ratio
[HR] 1.03, 95% CI 1.02 to 1.05, p ⬍ 0.001). Furthermore, OSA (defined by an apnea-hypopnea index ⱖ5)
was a strong predictor of incident AF (HR 2.18, 95% CI
568
Gami et al.
OSA, Obesity, and AF
JACC Vol. 49, No. 5, 2007
February 6, 2007:565–71
Risk of Incident Atrial Fibrillation, Univariate Model
Table 2
Risk of Incident Atrial Fibrillation, Univariate Model
Age (per 10 yrs)
HR
95% CI
2.11
1.85–2.41
p Value
⬍0.001
Male gender
1.86
1.22–2.85
0.004
Hypertension
2.85
2.02–4.02
⬍0.001
5.15
3.56–7.44
⬍0.001
Coronary artery disease
Heart failure
11.76
7.6–18.20
⬍0.001
History of smoking
1.82
1.24–2.66
0.002
Diabetes mellitus
2.50
1.66–3.78
⬍0.001
Body mass index (per 1 kg/m2)
1.03
1.02–1.05
⬍0.001
Obstructive sleep apnea
(apnea–hypopnea index ⱖ5)
2.18
1.34–3.54
0.002
Apnea–hypopnea index (per 1 event/h)*
1.31
1.14–1.50
0.0001
Tertiles of apnea–hypopnea index
distribution
1.36
1.13–1.64
0.001
Arousal index (per 1 event/h)*
1.65
1.29–2.10
⬍0.001
Lowest nocturnal oxygen saturation
(per ⫺1%)*
3.08
1.72–5.54
⬍0.001
Mean nocturnal oxygen saturation
(per ⫺1%)†
1.30
2.82–4.77
0.05
*For a 1-U change in the logarithm. †For a 0.1-U decrease in the logarithm.
CI ⫽ confidence interval; HR ⫽ hazard ratio.
1.34 to 3.54, p ⫽ 0.002). Atrial fibrillation occurred in
114 of 2,626 patients (4.3%) with OSA and in 19 of 916
patients (2.1%) without OSA. Measures of OSA severity
were also strong predictors of incident AF: the log of the
apnea-hypopnea index (p ⬍ 0.001), tertiles of the apneahypopnea index (p ⫽ 0.001), log of the arousal index (p
⬍ 0.001), log of the lowest nocturnal oxygen saturation
(p ⬍ 0.001), and log of the mean nocturnal oxygen
saturation (p ⫽ 0.05).
Figure 1
Age-stratified, multivariate analyses. The regression
models showed a significant interaction between OSA and
age (p ⫽ 0.04). This was consistent with previous data
showing that age modifies the effect of OSA on cardiovascular outcomes, such that cardiovascular effects of OSA
manifest in individuals younger than about age 65, but not
in those who are older (27,28). These considerations provided the rationale for stratified analyses for subjects
younger than 65 years (3,053 of 3,542, 86%) and 65 years or
older (489 of 3,542, 14%). The cumulative probability of
incident AF was significantly greater for subjects ⬍65 years of
age with OSA compared with those without OSA (Fig. 1).
The results of multivariate Cox proportional hazards regression for subjects ⬍65 years of age and for subjects ⱖ65 years
of age are shown in Table 3. In subjects ⬍65 years of age,
established risk factors for AF (age, male gender, and
coronary artery disease) independently predicted incident
AF, as did body mass index (per 1 kg/m2, HR 1.07, 95% CI
1.05 to 1.10, p ⬍ 0.001). The decrease in nocturnal oxygen
saturation also was an independent predictor of incident AF
(for each 0.5-U change in the log, HR 3.29, 95% CI 1.35 to
8.04, p ⫽ 0.009). For subjects ⱖ65 years old, only heart
failure independently predicted incident AF (HR 7.68, 95%
CI 4.32 to 13.66, p ⬍ 0.001). In a multivariate regression
model that included only subjects with OSA, use of continuous positive airway pressure did not positively or negatively affect the incidence of AF. In multivariate regression
models that directly compared the effects of OSA and
obesity on incident AF, the conditions significantly predicted incident AF independently of each other (Fig. 2).
Incidence of AF Based on Presence or Absence of OSA
Cumulative frequency curves for incident atrial fibrillation (AF) for subjects ⬍65 years of age with
and without obstructive sleep apnea (OSA) during an average 4.7 years of follow-up. p ⫽ 0.002.
Gami et al.
OSA, Obesity, and AF
JACC Vol. 49, No. 5, 2007
February 6, 2007:565–71
Risk of Incident
Fibrillation,
Multivariate
Atrial Models
Risk of Incident Atrial
Table 3
Fibrillation, Multivariate Models
HR
95% CI
p Value
Age (per 10 yrs)
2.04
1.48–2.80
⬍0.001
Male gender
2.66
1.33–5.30
0.006
Coronary artery disease
2.66
1.46–4.83
0.001
Body mass index (per 1 kg/m2)
1.07
1.05–1.10
⬍0.001
Decrease in nocturnal oxygen
saturation (per ⫺1%)*
3.29
1.35–8.04
0.009
7.68
4.32–13.66
⬍65 yrs old
ⱖ65 yrs old
Heart failure
⬍0.001
*For a 0.5-U change in the logarithm of the difference between awake oxygen saturation and mean
nocturnal oxygen saturation.
CI ⫽ confidence interval; HR ⫽ hazard ratio.
Discussion
This large cohort study provides important insights into the
relationships between obesity, OSA, and AF. First, it
confirms recent data showing that obesity is an independent
risk factor for incident AF (5,6). It extends this information
with the novel finding that body mass index predicts
incident AF independently of OSA. Furthermore, the study
has shown for the first time that in individuals ⬍65 years of
age, OSA strongly predicts the incidence of AF within
about 5 years of its diagnosis, and that the degree of
nocturnal oxygen desaturation, which is an important
pathophysiological consequence of OSA, independently
correlates with the risk of incident AF.
Although recent studies showed an independent role of
obesity in predicting AF (5,6), other data suggest that this
relationship may not be evident in older patients (i.e., those
ⱖ65 years of age) (29,30). In this context, the findings in
our select population suggest that the mechanisms by which
obesity confers risk of AF may be more significant during
younger adult ages. In these individuals, for every increase of
5 kg/m2 (e.g., moving from normal weight to overweight, or
from overweight to mild obesity), the risk of incident AF
increased by 15%.
Similar differential effects of age on incident AF risk were
evident for OSA. The limitation of the predictive power of
OSA to adults ⬍65 years of age is consistent with previous
studies of OSA and cardiovascular morbidity and mortality
(27,28), which together suggest that the pathophysiological
consequences of OSA are less striking in older adults, in
whom other factors may have greater prognostic significance. Why OSA and obesity may be accompanied by an
increased risk of incident AF in individuals ⬍65 years of age
but not in older persons is unclear. Obesity and OSA are
likely to be present and established by middle age. It is
possible that when obesity and/or OSA are diagnosed at age
65 years or older, both conditions would already have been
present for many years before diagnosis, and any effect on
the incidence of AF already would be manifest. Subjects
with AF at the time of diagnosis of OSA were excluded
from our study—it is possible that if the effects of obesity
569
and OSA had not already led to AF by the age of 65, then
they were unlikely to do so as these older patients aged.
It is crucial for studies of obesity and cardiovascular risk
to identify OSA in the study populations because several
cardiovascular disease mechanisms in obese people can also
be attributable to occult OSA (10 –14). Although relationships between obesity and AF have been explored, no prior
study incorporated the presence or absence of OSA into
predictive models for AF during long-term follow-up.
Therefore, it remained unclear whether OSA was confounding relationships between obesity and AF. In the
present study, we were able to classify the presence or
absence of OSA based on the gold standard diagnostic test,
polysomnography, in a large study population. That obesity
remains a powerful predictor of incident AF, even after
controlling for OSA, in people ⬍65 years of age clarifies an
important question raised by previous studies (8).
Our finding that the magnitude of the decrease in
nocturnal oxygen saturation was independently predictive of
incident AF may provide insight into the putative mechanisms that link OSA to the development of AF. The
primary consequence of an apnea is hypoxemia, which
directly or indirectly (by promoting ischemia, sympathetic
activation, and inflammation) may be an important mechanistic link to cardiovascular complications. Indeed, previous investigations have implicated measures of oxygen
desaturation as likely mediators of the interaction between
OSA and AF. One study found a direct relationship
between incident AF and the oxygen desaturation index, but
not the apnea-hypopnea index, before hospital discharge in
patients undergoing coronary bypass surgery (31). A second
study found that a very high rate of recurrent AF after
electrical cardioversion in patients with untreated OSA was
Figure 2
Incidence of AF Based on the
Severity of OSA and Obesity
Cumulative frequency of incident atrial fibrillation (AF) during an average 4.7
years of follow-up, based on interactions between the body mass index (BMI)
and the apnea-hypopnea index (AHI). An AHI ⬍5 represents no obstructive
sleep apnea (OSA), an AHI 5 to 40 represents mild to moderate OSA, and an
AHI ⬎40 represents severe OSA. A BMI ⬍25 represents normal weight, a BMI
25 to 30 kg/m2 represents overweight, and a BMI ⬎30 kg/m2 represents
obesity.
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Gami et al.
OSA, Obesity, and AF
directly related to the magnitude and duration of nocturnal
oxygen desaturation (32). Our findings are consistent with
this literature. Identification of the specific mechanisms that
initiate or promote AF in OSA patients may have implications for targeted intervention strategies.
Multiple studies have described the independent association of OSA with diastolic dysfunction (19 –22). The
pattern of mitral early and late inflow velocities (E-A ratio)
and the duration of mitral early inflow (deceleration time)
were both shown to correlate with the magnitude and
duration of oxygen desaturation during sleep in OSA
patients (19 –22). Diastolic dysfunction may lead to increases in left atrial size, which has been shown to powerfully predict incident AF (29). Left atrial size also may be
increased in OSA patients because of stretch of the thinwalled atria during the repetitive Mueller maneuvers (attempted inspirations against the obstructed airway) that
characterize obstructive apneic sleep. These obstructed inspirations generate wide fluctuations in negative intrathoracic pressure, which elicit changes in cardiac transmural
pressures and increased cardiac wall stress (15–17). Thus,
mechanisms by which OSA increases the risk of AF may
include diastolic dysfunction and increased left atrial size.
However, the present study cannot address this directly.
Other mechanisms may also contribute to the increased
risk of AF in people with OSA. Rapid swings in cardiac
transmural pressures may activate atrial stretch-sensitive ion
channels, particularly in anatomically vulnerable sites such
as the pulmonary vein ostia, that are implicated in the
initiation of AF. Another potential mechanism is the marked
autonomic imbalance that occurs in OSA (18). The severe
elevations of sympathetic activity during apneas may activate
atrial catecholamine-sensitive ion channels and result in
focal discharges that initiate AF. Furthermore, the marked
vagal predominance at the end of apneas, which is associated with bradyarrhythmias, may change conduction properties of the atria to promote AF. Lastly, OSA is associated
with systemic inflammation (14), which may increase the
risk of AF via effects on atrial myocardial remodeling (33).
The finding that the use of continuous positive airway
pressure did not reduce the risk of AF deserves mention.
The method by which we classified use of continuous
positive airway pressure was limited because of the retrospective nature of the study. The assessment relied on the
subjective reporting of continuous positive airway pressure
use and the documentation of such in the medical record. It
could not account for its frequency (days per week or hours
per night) of use, compliance, or effectiveness at abolishing
apneas because the majority of subjects were cared for before
the widespread use of continuous positive airway pressure
devices that incorporate time-of-use counters. Intuitively,
the use of continuous positive airway pressure, which
decreases apneic events and reduces hypoxemia, would be
expected to protect from incident AF. However, continuous
positive airway pressure tends to be prescribed and used
more often in patients with more severe OSA, as was also
JACC Vol. 49, No. 5, 2007
February 6, 2007:565–71
evident in our study. Thus, patients with more severe OSA
were more likely to be using continuous positive airway
pressure therapy, which may have confounded the association between its use and incident AF. This is especially true
because compliance with and effectiveness of the treatment
could not be assessed and accounted for in the predictive
models.
A limitation of the study is that the sample consisted of
patients referred to a sleep clinic because of a suspected sleep
disorder. Extending the findings to the general population is
limited by the fact that characteristics of OSA in the general
population may differ from those in this referral population.
However, there are major economic and logistic obstacles to
confirming the diagnosis of OSA with the gold standard
test, polysomnography, in large population studies. Thus,
evaluating the questions assessed in the present study in the
population at large is limited by resources and only rarely
possible. In fact, over 80% of individuals with OSA in the
general population remain undiagnosed (34). Another limitation is the method of ascertainment of AF during
follow-up, which would fail to identify patients with asymptomatic AF who did not seek medical attention. This is a
common limitation in studies of AF, even in prospective
studies that use electrocardiographic monitoring. Ours was
a retrospective cohort study, and thus active electrocardiographic monitoring of study patients to identify incident AF
was not possible. We limited our study sample to individuals
who were Olmsted County residents to form a geographically circumscribed group of patients whose medical
follow-up would be limited mostly to our institution. The
details of the Mayo Medical Index were described earlier,
and this system should have been successful in including all
diagnosed episodes of AF during follow-up. Another potential limitation of the study is that the presence of OSA may
have resulted in greater follow-up, which could have led to
increased identification of AF in OSA patients. To address
this, we compared follow-up durations in these patients and
found that follow-up duration was unrelated to the presence
or severity of OSA. However, it remains possible that
patients with OSA had more medical encounters than
patients without OSA despite similar follow-up durations.
Lastly, there is the consideration that the risk of AF was
modified during follow-up by the progression of obesity and
development of OSA after an initially negative polysomnogram. It is possible that individuals without OSA at the
time of polysomnography gained weight during follow-up
and developed OSA, which if true in a substantial proportion of study patients could have limited our ability to
identify more robust associations between OSA and AF.
Conclusions. In this cohort of 3,542 people who underwent complete polysomnography, obesity, and the magnitude of nocturnal oxygen desaturation, which is an important pathophysiological consequence of OSA, were
independent risk factors for incident AF over an average of
about 5 years of follow-up. The risks conferred by obesity
JACC Vol. 49, No. 5, 2007
February 6, 2007:565–71
and OSA were independent of one another and limited to
people ⬍65 years of age.
Our results have important implications, first, for understanding the pathophysiology of AF, and second, for the
growing obesity epidemic, which is accompanied by a
significant increase in the number of individuals with OSA.
Based on our findings, both obesity and OSA may contribute to the looming epidemic of AF. Both conditions should
be recognized as independent risk factors for incident AF.
Treatments targeting obesity are imperative, and randomized controlled trials are required to evaluate the efficacy of
the treatment of OSA for preventing or treating AF.
Acknowledgments
The authors thank the members of the Mayo Clinic
Cardiovascular Research Studies Unit for their excellent
assistance with data management.
Reprint requests and correspondence: Dr. Virend K. Somers,
200 First Street SW, Rochester, Minnesota 55905. E-mail:
[email protected].
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